This invention refers to the capture, organization, storage and presentation of expert knowledge and the facilitation of the interaction with that knowledge by other professionals. This invention also produces end-products relating to the professional""s activities.
An expert is considered to be someone who has extensive knowledge on a given topic. Traditionally, if an expert wanted to record his knowledge into a usable format, he would write a book or article, put thoughts into a diary or file, etc. This xe2x80x9cpublishedxe2x80x9d knowledge would then be available for use by others who could learn from it and utilize it.
In the 1970s and 1980s, the continuing development of computing technique and power resulted in a software category of xe2x80x9cexpert systemsxe2x80x9d. The purpose behind expert systems is to take the expert""s knowledge of a situation, event, circumstance, etc. and translate that into a software application, usable by other professionals who are working on a similar problem. In using the expert system, the professional follows through the series of steps, as designated by the software, to both xe2x80x9ccorrectlyxe2x80x9d apply an expert process against the event under consideration and to reach an appropriate conclusion to that event.
The key to the expert system is that the steps that the software presents to the user/professional are based on a series of rules whereby the conduct or answers within one step determines what the next step should be. Once these rules are established within the software application, the user/professional is required to follow the steps as they are presented to him in order for the software (and therefore the user/professional) to function correctly. The user/professional found that differences in approach, timing, unaccounted factors, etc. led to so many exceptions to the stepwise rules of the expert system, that the system became essentially ineffective.
As well, the rules of presentation for the process often closely reflect the personal strategic approach to the situation of the authoring expert. This means it does not necessarily meet the strategic needs and expectations of the professionals who uses the expert system.
Furthermore, any changes to the expert system to reflect alternative models, unaccounted circumstances, and just general evolution of knowledge about the event, are very labor intensive and expensive to perform, leaving the expert system essentially static once it has been implemented. Over time, the expert system is also increasingly limited in utility. The user/professional has determined that expert systems lack important flexibility and are lacking from a conceptual standpoint. As a result, expert systems have failed.
Having recognized the shortcomings of expert systems, the software industry introduced Decision Support Software (DSS). In DSS, the user is asked to gather facts and supply that information to the software application. The DSS then uses that information to determine the likelihood that the professional can expect a particular outcome or may be dealing with a certain type of event. However, the wizened professional knows the factors and outcomes before they begin inputting information into the DSS. That makes the use of the DSS a trite exercise. Then when the DSS is needed for support on more complex issues, the professional finds the DSS fails to account for many of the possible factors that contribute to that particular situation. It also comes up short in recognizing the many permutations that the situation entails.
The DSS strategy, much like the expert system, is expensive and costly to maintain and upgrade. DSS also does not provide the required necessary flexibility because it too is rule bound in determining likely answers to a problem or outcomes of events. So it, too, is a conceptual failure except in cases where a DSS is required because expertise is rare and/or the financial backing is strong allowing the DSS knowledge base maintenance and upgrade cycles to continue.
Stepping into the gap left by expert systems and decision support software is specialty report writing software products that attempt to alleviate the administrative and bureaucratic burden faced by professionals. The strategy behind these software applications is that the professional answers a series of questions after which the software generates a narrative report. However, these software applications have drawn too closely on expert systems approach and have therefore inherited their technological shortcomings, i.e., predetermined step-wise use and lack of flexibility in content coverage.
Specialty report writers introduce a new problem. The narrative generation capability is usually built around standard or stock paragraphs or sentences where the answers to the questions determine the text to pull in or merge into the final document. The strategy here is a sophisticated hybrid of word-processing cut and paste and mail merge technologies. These strategies work best when the resulting documents are standard forms for an industry. Legal, financial, and insurance disciplines have benefited most from this software category.
To make this strategy work, programmers must build an infrastructure for document generation including the construction of databases, user interfaces including forms which display all the questions, and the building of the merge functions which drive the document generation. Much like expert systems and decision support, development, maintenance, and upgrade cycles are very expensive. The resulting software is, once again, relatively static and limits the creative approach of the user/professional. And they produce rigid, stratified final documents that limit the number of disciplines to which this kind of solution finds applicability.
With no other solution in sight, most professionals are left to their word-processors in order to produce their necessary communications. Unfortunately, communication skills vary across any given professional group. Further complicating the issue is that management must interpret the content of a report because different writers/professionals use a multiplicity of terms and different approaches when writing a reportxe2x80x94even if the facts are the same. Comparison of reports across different situations is therefore extremely laborious. As well, each professional, depending on training, experience, and specialty, may focus on different aspects of a situation leaving gaps in coverage. In fact, in many cases the time and effort necessary to fully document a situation or event is an increasingly unrealistic and expensive burden. It is therefore difficult for management or other recipients, in reviewing a compiled report, to potentially catch the full understanding of the situation.
This problem has therefore become one of managing information and knowledge. The first attempts at managing knowledge can be referred to as disaggregation strategies. By breaking the professional process into its components, in essence the facts or information the professional was gathering, and perhaps the conclusions they were drawing, organizations have turned the professional process into information that can be readily accessed and shared. Put more simply, organizations develop checklists for their professionals to complete.
This strategy has serious downsides. First, to computerize the checklists still requires the expense of programmers to build the data structures and the interfaces. Again, changes are time consuming and costly.
Furthermore, the report writing side of the process is usually eliminated because of its inherent complexity. This has two repercussions: 1) the professional has to complete the checklist but still has to write a report. There is no time saving in this process, only a doubling of the work; 2) or only checklist completion is required for task completion. This strategy ultimately reduces the scope and amount of information gathered and produces less knowledge about events as opposed to more.
This strategy for managing knowledge therefore falls short.
More recently, knowledge management has come into existence as a software category whose purpose is to facilitate an organization""s ability to share its knowledge more effectively.
There are three major areas of focus in knowledge management: 1) management of information sources or resources; 2) management of expertise; 3) management of collaboration among professionals.
In managing resources, the assumption is that every document, picture, web page, video clip, e-mail, etc. produced by an organization contains valuable information that may be useful to someone elsewhere in the organization. In order to lead people to the resource, the item must be discoverable and retrievable. Knowledge Management software automates the categorization of these resources by key words, type, phrases, elements, etc. The person searching for the information can navigate or search the categories to find an appropriate information resource. Still, when they get there, they do not know until they review the resource, if the information is of help to them. This use of knowledge management may help in discovery and retrieval but it offers no guarantee of utility.
In managing expertise, knowledge management recognizes that large organizations have a great deal of hidden knowledge and experience, i.e., people with skills that are not widely known for various reasons. Knowledge Management relies upon a database of people""s expertise to allow those in need to search out required skills within the organization that may be able to contribute to a solution to a problem. This allows the knowledge seeker to know whom to contact within the organization. However, this paradigm expects that the expert has the time to commit to the seeker, is motivated to offer advisement, and is able to communicate his skill set. Disparities in geography and time zones can limit the effectiveness of this strategy.
With respect to collaboration, knowledge management attempts to reduce disparities in geography and time by providing a software solution that allows members of a group to communicate despite differences in geography or time zones.
Overall, knowledge management has yet to crack the barrier of direct capture of expertise and thus perpetuates the lack of direct support for the professional process within a given discipline.
It is an object of the present invention to provide an improved method for the capture, organization, storage, and presentation of expert knowledge.
It is a further object of the present invention to provide an improved system for the facilitation of the interaction with expert knowledge by other professionals.
It is another object of the present invention to generate narratives and other elements such as graphics and tables, to provide the necessary end-products that the professional requires as part of his ongoing activities.
Thus, in sharp contrast to the prior art, the present invention is based on the practice of Knowledge Architecture.
Knowledge Architecture is focused on the capture and use of an expert""s knowledge in any profession without limitation. In order to capture an expert""s knowledge, an expert or group thereof must decide on the focus or specific subject matter that they wish to capture. For instance, a speech and language professional may wish to capture his knowledge of the assessment process for autistic children. By capturing this knowledge, he will be able to eventually share his expertise and support the assessment process as conducted by other professionals in the speech and language discipline.
After deciding the subject matter of the expertise, the expert(s) begins to organize and define his knowledge. This requires translating his knowledge into a series of topics and questions that define his professional process. With experience, an expert increasingly organizes his understandings into a hierarchical set of topics that become a guiding map for his thought processes. For each topic within the hierarchy, the professional has a series of questions he will ask himself, should the topic be relevant to the situation. These questions cover the facts he observes, the information he needs to gather, choices that he must make, judgements that he must consider, or relationships between factors that might exist.
For instance, the speech and language professional looks at a number of different subdiscipline areas when assessing a child (e.g., hearing, voice, fluency, language). He gathers information on physical factors, performance, behavior, and capability. He also makes judgements as to whether the child is performing to expectation relative to peer groups. Selection of whether to analyze a subdiscipline area is at the discretion of the professional so not all children are assessed in all areas each time.
These questions and the discretion in application represent the analytical capability that the expert brings to the situation. This analytical capability is the single most important asset the expert brings to a situation.
The combination of hierarchically organized topics and associated questions can be called xe2x80x9cstructured contentxe2x80x9d. The present invention allows the expert to record his structured content as raw material. This raw material structured content is then stored in a data structure.
Structured content (as comprised of topics and questions) often is only one part of the ongoing activities of the professional. Professionals usually communicate their findings and opinions, often in the form of a narrative.
Following from the topics and questions, the present invention offers the opportunity to script narratives (and accompanying graphs and charts) that draw upon the answers to the questions. The expert can therefore design an end-product that communicates his findings and opinions.
Much like the structured content of the topics and questions, the end-product can be organized as structured content, this time in the form of the hierarchy used when writing a document. The breakdown reflects the infrastructure of a document consisting of sections, subsections, paragraphs, sentences, phrases, and snippets (the written word). All levels above the snippet are organizing levels that represent a hierarchy for the written word.
Again, the present invention offers a methodology for developing an end-product and storing this as raw structured content. The end-product methodology primarily focuses on a narrative generating capability that offers very sophisticated control over the way narrative is brought together right down to the micro-level. This unique methodology of narrative generation results in a very fluid and dynamic system for pulling together text that sounds more natural than that produced by prior generations of specialty report writers.
As raw material, the two forms of structured content captured within the present invention are now usable by other professionals in that industry. To make it available to them, the captured raw material must be prepared and presented in the form of a specialized software application (SSA). This present invention has a methodology for conversion of the raw material into a presentable format. The raw material is prepared to meet the display and other requirements of this present invention""s proprietary interface, which is the core of the SSA, for presentation of and interaction with the structured content.
With respect to topics and questions, the proprietary interface has two display requirements. In one region on the screen is a table of contents representing the topic hierarchy (also referred to as the dialog tree). A second region on the screen is used to display the questions for a selected topic (also known as the topic area).
The raw structured content of the topics is converted into the hierarchy of topics that acts as the table of contents for the user/professional. The preparation process uses the raw question material to determine question positions and results are stored along with other key question information. When a user/professional selects a topic inside the SSA, the processed question information is used to dynamically build and present questions to the user.
Since questions are presented to the user to be answered, this present invention also automates the preparation of a storage structure to hold the answers to the user/professional questions.
With respect to the potential end-products of an SSA, the proprietary interface offers the user/professional an opportunity to select end-products (or parts thereof) he wishes to generate. When preparing the raw material of the end-product, the various end-products available to the SSA are stored in a data structure accessible by the SSA. The end-product generation logic is also converted into a format that is usable within the SSA and stored in an appropriate structure accessible by the SSA.
Having processed all the raw material into a format that is usable by the present invention""s proprietary interface, the user/professional opens the SSA and is presented with the processed structured content via the proprietary interface. When the user/professional is presented with questions and information, the user/professional interacts with those questions. Any answers he provides are stored in a data structure.
Once the user/professional is satisfied he has answered all questions relevant to the subject under consideration, the user/professional requests the generation of an end-product. As part of the standard interface, the user/professional is able to select the end-products he wishes to generate. Having selected the end-products, or portions thereof, the SSA then proceeds to draw upon the answers to the questions and dynamically build narrative and other elements of an end-product.
The end-product, once completely generated, is saved as a file external to the SSA. The SSA can launch an appropriate external application and open the generated end-product into that external application (e.g., word-processor). At that point, the user/professional can make any free-form changes to the end-product he so desires.
The present invention has the unique feature of allowing the design of the structured content to be developed with no or minimal intervention by software programmers. In other words, the expert(s) can build the SSA and its end-products without the assistance of software programmers. And the expert has full and simplified control over the evolution of changes to the product and the timing of release of those changes.
Knowledge Architecture, relative to the prior art, brings together the best that the prior art has offered, overcomes its serious shortcomings, and establishes new practices in software development.
For instance, expert systems attempted to capture expert knowledge but considered expert knowledge the application of rules related to a professional process. In Knowledge Architecture, the rules of the professional process are assumed known by the professional and are therefore not considered to be appropriate for inclusion.
Instead, it is assumed the expert requires support, not specific guidance, for his observational and judgmental acts. These observational and judgmental acts are expressed within Knowledge Architecture as questions, importantly, without rules as to how or when in the professional process they should be asked or applied.
Having freed Knowledge Architecture from the requirement of having to establish rules related to the navigation of the application, conceptually the expert must focus in on the theoretical organization and subsequent questions that are used in the conduct of the professional endeavor. This theoretical organization, or hierarchy of topics, and their related questions, is the xe2x80x9cstructuredxe2x80x9d content that Knowledge Architecture attempts to capture and use. By allowing the expert to directly input this content into a software application, the normal costs and efforts associated with this type of knowledge capture and software development is significantly reduced.
Remaining consistent with the mission of Knowledge Architecture, when the structured content is presented to an SSA user, he can navigate anywhere within the hierarchy and answer any question in any order. With no rules to follow, the presentation of knowledge (i.e., structured content) via Knowledge Architecture achieves the standard of an open architecture for knowledge.
This stands in sharp contrast to the expert systems wherein rules decide, based on one set of answers by the user/professional, what questions should next appear to the same user/professional. Problems arise when the sequence of questions and answers in the software do not fully adhere to the situation. The user/professional is still required to provide an answer he does not necessarily feel fits the situation. Within Knowledge Architecture, should exceptions to the rules occur, the user/professional simply moves on and/or makes free-form changes to the end-product that account for the exception. This Knowledge Architecture philosophy lends to the user/professional, great flexibility in the use and application of the knowledge contained with the SSA.
Moreover, given that no rules of usage need to be implemented and the expert inputs the structured content himself, as knowledge evolves, the expert can sit down and evolve the SSA at will. Knowledge Architecture allows the knowledge base within the SSA to grow in an efficient and cost-effective manner.
With respect to Decision Support Systems, they rely upon rules to determine possible outcomes or the likelihood of an event. As with Expert Systems, this strategy carries a maintenance, upgrade, and usage burden. Once again, Knowledge Architecture bypasses all the inherent burdens that come with the implementation of a rules based system. Furthermore, Knowledge Architecture assumes the user/professional knows what decisions need to be made, the factors that go into the decision making process, and should maintain the discretion of making or not making those decisions. Knowledge Architecture understands that the seasoned user/professional has likely considered all these factors, and come to his conclusions prior to ever explicitly answering any questions within a piece of software.
These factors give the expert his authentication and credence to his credentials. Knowledge Architecture supports these elements of the professional""s intellectual process by not making judgements on behalf of the professional, thereby overcoming an important psychological barrier.
Specialty report writing software shares with Knowledge Architecture a purpose of producing an end-product (a narrative report). Like Knowledge Architecture, specialty report writing software moves beyond the word-processor as a knowledge resource. Both strategies produce a consistent infrastructure in terms of the sequence of presentation of information via a narrative report. This makes the management task of searching for relevant material and comparison between end-products much more manageable. Both strategies also try to raise the level of quality of communication. However this is where the similarities end.
Knowledge Architecture in the present invention directly captures structured content from the expertxe2x80x94i.e., the knowledge capture phase does not require programming teams.
In the present invention, Knowledge Architecture uses an open architecture for the presentation of knowledge wherein access to structured content is freeform and rule free. Most if not all the report writing systems available direct the path of questioning that the user/professional sees and experiences.
In the present invention, there is an automated preparation process that converts raw structured content into a finished product as compared to the available report writing software which requires teams of programmers to convert raw material into the finished software product.
In the present invention, the detailed control over the narrative leads to a very fluid and dynamic narrative generation capability. Many report writers suffer from narrative generation that is very static and rigid, following from reliance upon preset paragraphs or sentences. As such, it has worked for legal, financial, and insurance documentation. However, for the professional whose communications are less rigid and an inherent part of their professional identity, the stilted, rigid writing approach offered by these specialty report writers has not offered the right solution.
The process within the present invention goes well beyond a sophisticated mail merge or cut and paste strategy. The present invention includes detailed control over the narrative, allowing the sentence to be manipulated down to the punctuation. Such fine control over the written word, along with the supplementary tools for narrative generation, produces an excellent written narrative. As a result, the present invention produces a fluid, natural style of narrative that closely reflects the professionally written document. With a higher quality of narrative, more professionals will welcome and adopt the administrative support that Knowledge Architecture offers.
The present invention improves upon the disaggregation strategy of designing a database with an interface to capture professional information. The greatest hurdle faced in this disaggregation strategy, again, is the time and cost burden of bringing in programming teams to build the software system. Once again, since content is directly provided and controlled by the expert, the time and cost problem is resolved, the scope of potential questioning is extended, and barriers are broken with respect to the amount of information that can be gathered. In addition, the present invention includes the narrative generation capability that can be used to more effectively communicate the results of the information gathering process, all the while relieving the user/professional of the administrative burden that is not inherently available within the disaggregation strategy.
With respect to Knowledge Management, Knowledge Architecture is an entirely different domain. First, Knowledge Management organizes previously existing knowledge stored in various structured and unstructured forms such as documents, web pages, etc. Knowledge Architecture of the present invention does not categorize or organize existing information resources. Knowledge Architecture attempts to capture the organization and structure of the tacit, implicit, and hidden knowledge that resides within experts.
Second, Knowledge Management manages expertise by offering searchable databases on what a person""s knowledge encompasses. This might include pointers to documents written by the expert. However, the database contains knowledge xe2x80x9cabout their expertisexe2x80x9d, it does not necessarily contain xe2x80x9cdetailed knowledge of their expertisexe2x80x9d. For instance, the software may know that an individual has expertise in building roads through a jungle however there may be no information describing the process of building a road in the junglexe2x80x94which he would have.
The purpose of this type of Knowledge Management software would be to offer the location of the individual so that others building roads under similar circumstances could contact him. Of course, transfer of knowledge using this Knowledge Management strategy depends on making contact with the expert and the quality of the communication from the expert.
With Knowledge Architecture, an SSA could exist that has captured the knowledge of the expert and is readily and consistently available regardless of the availability of the expert. As a result, detailed information on the considerations related to materials and method of building a road in a jungle would be available through use of the present invention. Not only that, but by using the SSA, the knowledge of road building is disaggregated into its components offering a massive qualitative data store on the process of road building.
Finally, Knowledge Management facilitates collaboration of persons across time and geography. It allows persons to collaborate on a problem, solution, documents, etc. The present invention also facilitates collaboration but the primary focus of its collaborative capability is with respect to disaggregated knowledge, i.e., Knowledge Architecture of the present invention facilitates collaboration on the gathering of specific information by persons separated due to time or geography. Furthermore, the present invention produces an end-product such as a document that could then be the subject of a collaborative event within the domain of Knowledge Management.
Knowledge Architecture of the present invention supplements and compliments the domain of Knowledge Management.
Overall, Knowledge Architecture as implemented via the present invention selects only appropriate best practices of the prior art, pushing beyond any previous limitations in strategy and overcoming the shortcomings in execution inherent in the prior art. Knowledge Architecture and the present invention create a whole new category of business process and technology making the development of specialized software applications a highly manageable and affordable goal for any organization.